Crash Course in Python for Machine Learning Developers

By Jason Brownlee on August 21, 2019 in Python Machine Learning 24

You do not need to be a Python developer to get started using the Python ecosystem for machine learning.

As a developer who already knows how to program in one or more programming languages, you are able to pick up a new language like Python very quickly. You just need to know a few properties of the language to transfer what you already know to the new language.

In this post, you will get a crash course in Python and the core libraries needed for machine learning. Namely: NumPy, MatPlotLib and Pandas.

This will be just enough information to help you read and understand code Python code examples for machine learning and start developing your own scripts. If you already know a little Python, this post will be a friendly reminder for you.

Kick-start your project with my new book Machine Learning Mastery With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

Update Mar/2017: Updated all print statements to work with Python 2 and Python 3.

Crash Course in Python for Machine Learning Developers
Photo by John Clouston, some rights reserved.

Python Crash Course

When getting started in Python you need to know a few key details about the language syntax to be able to read and understand Python code. This includes:

Assignment
Flow Control
Data Structures
Functions

We will cover each of these topics in turn with small standalone examples that you can type and run.

Remember, whitespace has meaning in Python.

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Assignment

As a programmer, assignment and types should not be surprising to you.

Strings

# Strings
data = 'hello world'
print(data[0])
print(len(data))
print(data)

# Strings

data = 'hello world'

print(data[0])

print(len(data))

print(data)

Running the example prints:

h
11
hello world

hello world

Numbers

# Numbers
value = 123.1
print(value)
value = 10
print(value)

# Numbers

value = 123.1

print(value)

value = 10

print(value)

Running the example prints:

123.1
10

123.1

Boolean

# Boolean
a = True
b = False
print(a, b)

# Boolean

a = True

b = False

print(a, b)

Running the example prints:

(True, False)

1	(True, False)

Multiple Assignment

# Multiple Assignment
a, b, c = 1, 2, 3
print(a, b, c)

# Multiple Assignment

a, b, c = 1, 2, 3

print(a, b, c)

Running the example prints:

(1, 2, 3)

(1, 2, 3)

No Value

# No value
a = None
print(a)

# No value

a = None

print(a)

Running the example prints:

None

None

Flow Control

There are three main types of flow control that you need to learn: If-Then-Else conditions, For-Loops and While-Loops.

If-Then-Else Condition Example

value = 99
if value >= 99:
	print('That is fast')
elif value > 200:
	print('That is too fast')
else:
	print('That that is safe')

value = 99

if value >= 99:

print('That is fast')

elif value > 200:

print('That is too fast')

else:

print('That that is safe')

Running this example prints:

That is fast

1	That is fast

For-Loop Example

# For-Loop
for i in range(10):
	print(i)

# For-Loop

for i in range(10):

print(i)

Running this example prints:

While-Loop Example

# While-Loop
i = 0
while i < 10:
	print(i)
	i += 1

# While-Loop

i = 0

while i < 10:

print(i)

i += 1

Running this example prints:

Data Structures

There are three data structures in Python that you will find the most used and useful. They are tuples, lists and dictionaries.

Tuple Example

Tuples are read-only collections of items.

a = (1, 2, 3)
print(a)

1 2	a = (1, 2, 3) print(a)

Running the example prints:

(1, 2, 3)

(1, 2, 3)

List Example

Lists use the square bracket notation and can be index using array notation.

mylist = [1, 2, 3]
print("Zeroth Value: %d" % mylist[0])
mylist.append(4)
print("List Length: %d" % len(mylist))
for value in mylist:
	print(value)

mylist = [1, 2, 3]

print("Zeroth Value: %d" % mylist[0])

mylist.append(4)

print("List Length: %d" % len(mylist))

for value in mylist:

print(value)

Running the example prints:

Zeroth Value: 1
List Length: 4
1
2
3
4

Zeroth Value: 1

List Length: 4

Dictionary Example

Dictionaries are mappings of names to values, like a map. Note the use of the curly bracket notation.

mydict = {'a': 1, 'b': 2, 'c': 3}
print("A value: %d" % mydict['a'])
mydict['a'] = 11
print("A value: %d" % mydict['a'])
print("Keys: %s" % mydict.keys())
print("Values: %s" % mydict.values())
for key in mydict.keys():
	print(mydict[key])

mydict = {'a': 1, 'b': 2, 'c': 3}

print("A value: %d" % mydict['a'])

mydict['a'] = 11

print("A value: %d" % mydict['a'])

print("Keys: %s" % mydict.keys())

print("Values: %s" % mydict.values())

for key in mydict.keys():

print(mydict[key])

Running the example prints:

A value: 1
A value: 11
Keys: ['a', 'c', 'b']
Values: [11, 3, 2]
11
3
2

A value: 1

A value: 11

Keys: ['a', 'c', 'b']

Values: [11, 3, 2]

Functions

The biggest gotcha with Python is the whitespace. Ensure that you have an empty new line after indented code.

The example below defines a new function to calculate the sum of two values and calls the function with two arguments.

# Sum function
def mysum(x, y):
	return x + y

# Test sum function
print(mysum(1, 3))

# Sum function

def mysum(x, y):

return x + y

# Test sum function

print(mysum(1, 3))

Running the example prints:

4

NumPy Crash Course

NumPy provides the foundation data structures and operations for SciPy. These are arrays (ndarrays) that are efficient to define and manipulate.

Create Array

# define an array
import numpy
mylist = [1, 2, 3]
myarray = numpy.array(mylist)
print(myarray)
print(myarray.shape)

# define an array

import numpy

mylist = [1, 2, 3]

myarray = numpy.array(mylist)

print(myarray)

print(myarray.shape)

Running the example prints:

[1 2 3]
(3,)

1 2	[1 2 3] (3,)

Access Data

Array notation and ranges can be used to efficiently access data in a NumPy array.

# access values
import numpy
mylist = [[1, 2, 3], [3, 4, 5]]
myarray = numpy.array(mylist)
print(myarray)
print(myarray.shape)
print("First row: %s" % myarray[0])
print("Last row: %s" % myarray[-1])
print("Specific row and col: %s" % myarray[0, 2])
print("Whole col: %s" % myarray[:, 2])

# access values

import numpy

mylist = [[1, 2, 3], [3, 4, 5]]

myarray = numpy.array(mylist)

print(myarray)

print(myarray.shape)

print("First row: %s" % myarray[0])

print("Last row: %s" % myarray[-1])

print("Specific row and col: %s" % myarray[0, 2])

print("Whole col: %s" % myarray[:, 2])

Running the example prints:

[[1 2 3]
 [3 4 5]]
(2, 3)
First row: [1 2 3]
Last row: [3 4 5]
Specific row and col: 3
Whole col: [3 5]

[[1 2 3]

[3 4 5]]

(2, 3)

First row: [1 2 3]

Last row: [3 4 5]

Specific row and col: 3

Whole col: [3 5]

Arithmetic

NumPy arrays can be used directly in arithmetic.

# arithmetic
import numpy
myarray1 = numpy.array([2, 2, 2])
myarray2 = numpy.array([3, 3, 3])
print("Addition: %s" % (myarray1 + myarray2))
print("Multiplication: %s" % (myarray1 * myarray2))

# arithmetic

import numpy

myarray1 = numpy.array([2, 2, 2])

myarray2 = numpy.array([3, 3, 3])

print("Addition: %s" % (myarray1 + myarray2))

print("Multiplication: %s" % (myarray1 * myarray2))

Running the example prints:

Addition: [5 5 5]
Multiplication: [6 6 6]

1 2	Addition: [5 5 5] Multiplication: [6 6 6]

There is a lot more to NumPy arrays but these examples give you a flavor of the efficiencies they provide when working with lots of numerical data.

Matplotlib Crash Course

Matplotlib can be used for creating plots and charts.

The library is generally used as follows:

Call a plotting function with some data (e.g. plot()).
Call many functions to setup the properties of the plot (e.g. labels and colors).
Make the plot visible (e.g. show()).

Line Plot

The example below creates a simple line plot from one-dimensional data.

# basic line plot
import matplotlib.pyplot as plt
import numpy
myarray = numpy.array([1, 2, 3])
plt.plot(myarray)
plt.xlabel('some x axis')
plt.ylabel('some y axis')
plt.show()

# basic line plot

import matplotlib.pyplot as plt

import numpy

myarray = numpy.array([1, 2, 3])

plt.plot(myarray)

plt.xlabel('some x axis')

plt.ylabel('some y axis')

plt.show()

Running the example produces:

Simple Line Plot in Matplotlib

Scatter Plot

Below is a simple example of creating a scatter plot from two-dimensional data.

# basic scatter plot
import matplotlib.pyplot as plt
import numpy
x = numpy.array([1, 2, 3])
y = numpy.array([2, 4, 6])
plt.scatter(x,y)
plt.xlabel('some x axis')
plt.ylabel('some y axis')
plt.show()

# basic scatter plot

import matplotlib.pyplot as plt

import numpy

x = numpy.array([1, 2, 3])

y = numpy.array([2, 4, 6])

plt.scatter(x,y)

plt.xlabel('some x axis')

plt.ylabel('some y axis')

plt.show()

Running the example produces:

Simple Scatter Plot in Matplotlib

There are many more plot types and many more properties that can be set on a plot to configure it.

Pandas Crash Course

Pandas provides data structures and functionality to quickly manipulate and analyze data. The key to understanding Pandas for machine learning is understanding the Series and DataFrame data structures.

Series

A series is a one-dimensional array where the rows and columns can be labeled.

# series
import numpy
import pandas
myarray = numpy.array([1, 2, 3])
rownames = ['a', 'b', 'c']
myseries = pandas.Series(myarray, index=rownames)
print(myseries)

# series

import numpy

import pandas

myarray = numpy.array([1, 2, 3])

rownames = ['a', 'b', 'c']

myseries = pandas.Series(myarray, index=rownames)

print(myseries)

Running the example prints:

a    1
b    2
c    3

a 1

b 2

c 3

You can access the data in a series like a NumPy array and like dictionary, for example:

print(myseries[0])
print(myseries['a'])

1 2	print(myseries[0]) print(myseries['a'])

Running the example prints:

1
1

DataFrame

A data frame is a multi-dimensional array where the rows and the columns can be labeled.

# dataframe
import numpy
import pandas
myarray = numpy.array([[1, 2, 3], [4, 5, 6]])
rownames = ['a', 'b']
colnames = ['one', 'two', 'three']
mydataframe = pandas.DataFrame(myarray, index=rownames, columns=colnames)
print(mydataframe)

# dataframe

import numpy

import pandas

myarray = numpy.array([[1, 2, 3], [4, 5, 6]])

rownames = ['a', 'b']

colnames = ['one', 'two', 'three']

mydataframe = pandas.DataFrame(myarray, index=rownames, columns=colnames)

print(mydataframe)

Running the example prints:

   one  two  three
a    1    2      3
b    4    5      6

one two three

a 1 2 3

b 4 5 6

Data can be index using column names.

print("one column: %s" % mydataframe['one'])
print("one column: %s" % mydataframe.one)

1 2	print("one column: %s" % mydataframe['one']) print("one column: %s" % mydataframe.one)

Running the example prints:

one column: a    1
b    4

one column: a    1
b    4

one column: a 1

b 4

one column: a 1

b 4

Summary

You have covered a lot of ground in this post. You discovered basic syntax and usage of Python and four key Python libraries used for machine learning:

NumPy
Matplotlib
Pandas

You now know enough syntax and usage information to read and understand Python code for machine learning and to start creating your own scripts.

Do you have any questions about the examples in this post? Ask your questions in the comments and I will do my best to answer.

24 Responses to Crash Course in Python for Machine Learning Developers

MouChenghao September 9, 2016 at 9:51 pm #

I guess there is a wrong output in the last example, where the correct result should be like this:

one column: a 1
b 4
Name: one, dtype: int64
one column: a 1
b 4
Name: one, dtype: int64

Reply
- Jason Brownlee September 10, 2016 at 7:14 am #
  
  You are right, fixed. I have edited it for readability and broken the output. Sorry.
  
  Reply
Surya April 5, 2017 at 5:38 pm #

Hi Jason, on first string example printing data[0] would be “h” and not 4.

Reply
- Jason Brownlee April 9, 2017 at 2:33 pm #
  
  Thanks Surya.
  
  Reply
anon September 22, 2017 at 6:43 am #

Great post! Quick fix, the if-else-if is a bit wonky, ‘That is too fast’ will never print.

Reply
- Jason Brownlee September 23, 2017 at 5:32 am #
  
  Thanks.
  
  Reply
Ahmed July 14, 2018 at 6:33 pm #

How can I go to the next line without executing the code ?

>>> value = 99
>>> if value >= 99:
… print (‘That is fast’)
File “”, line 2
print (‘That is fast’)
^
IndentationError: expected an indented block
>>>

Reply
- Jason Brownlee July 15, 2018 at 6:10 am #
  
  Sounds like you need to indent the Python code correctly.
  
  This may help you copy code correctly with indenting:
  https://machinelearningmastery.com/faq/single-faq/how-do-i-copy-code-from-a-tutorial
  
  Reply
  - Ahmed July 15, 2018 at 4:30 pm #
    
    Actully I was doing that, but I thought that typing the code would give me better understanding of it rather than just copy it ( what do you think ?)
    so I’ve decided to type them but I don’t now how to go to next line without running the code
    
    Reply
    - Jason Brownlee July 16, 2018 at 6:11 am #
      
      Sounds good.
      
      Type the code into a text file. Then run the text file as a script from the command line, more help here:
      https://machinelearningmastery.com/faq/single-faq/how-do-i-run-a-script-from-the-command-line
      
      Reply
Ahmed July 17, 2018 at 2:16 pm #

Thanks a lot , very clear explanation

Reply
- Jason Brownlee July 17, 2018 at 2:32 pm #
  
  Thanks.
  
  Reply
Carlos Morente August 4, 2018 at 8:24 pm #

Hi Jason!

Just writing you to say that you made a little mistake in the “running the example prints” for Strings, the first output should be ‘h’ and not ‘4’.

Reply
- Jason Brownlee August 5, 2018 at 5:28 am #
  
  Thanks, fixed.
  
  Reply
Clint Laskowski December 11, 2018 at 10:58 pm #

Really nice (and quick) introduction to Python, NumPy, Matplotlib, and Pandas. Looking into your book now 🙂

Reply
- Jason Brownlee December 12, 2018 at 5:54 am #
  
  Thanks Clint.
  
  Reply
laz65 December 6, 2019 at 10:10 am #

many thanks jason 😉

Reply
- Jason Brownlee December 6, 2019 at 1:39 pm #
  
  You’re welcome.
  
  Reply
Robbie February 4, 2020 at 7:24 pm #

Hi Jason,

In the “IF-Then-Else Condition Example”, i would expect by changing the value to 300 the code should print ‘That is too fast’. However, the output is ‘That is fast’. Can you please explain?

#If-Then-Else Condition Example value = 300 if value >= 99: print('That is fast') elif value > 200: print('That is too fast') else: print('That that is safe')

1
2
3
4
5
6
7
8

#If-Then-Else Condition Example
value = 300
if value >= 99:
print('That is fast')
elif value > 200:
print('That is too fast')
else:
print('That that is safe')

#Output
That is fast

Reply
- Jason Brownlee February 5, 2020 at 8:05 am #
  
  The first condition was executed first and passed.
  
  You can re-order the if-statements or use a switch/case statement.
  
  Reply

MoJeeza February 6, 2020 at 7:26 am #

This should work..

#If-Then-Else Condition Example
value = 300
if value >200: # values over 200
	print('That is too fast')
elif value >= 99: # values between 99 and 200
	print('That is fast')
else:  # all values below 99
	print('That that is safe')

#If-Then-Else Condition Example

value = 300

if value >200: # values over 200

print('That is too fast')

elif value >= 99: # values between 99 and 200

print('That is fast')

else: # all values below 99

print('That that is safe')

Jason Brownlee February 6, 2020 at 8:34 am #

Nice work!

Reply

MoJeeza February 6, 2020 at 7:29 am #

Somehow the indentation was lost during paste

Reply
- Jason Brownlee February 6, 2020 at 8:34 am #
  
  I fixed it for you. by adding PRE tags.
  
  Reply

Navigation

Crash Course in Python for Machine Learning Developers

Python Crash Course

Need help with Machine Learning in Python?

Assignment

Strings

Numbers

Boolean

Multiple Assignment

No Value

Flow Control

If-Then-Else Condition Example

For-Loop Example

While-Loop Example

Data Structures

Tuple Example

List Example

Dictionary Example

Functions

NumPy Crash Course

Create Array

Access Data

Arithmetic

Matplotlib Crash Course

Line Plot

Scatter Plot

Pandas Crash Course

Series

DataFrame

Summary

Discover Fast Machine Learning in Python!

Develop Your Own Models in Minutes

Finally Bring Machine Learning To
Your Own Projects

More On This Topic

24 Responses to Crash Course in Python for Machine Learning Developers

Leave a Reply Click here to cancel reply.

Navigation

Python Crash Course

Need help with Machine Learning in Python?

Assignment

Strings

Numbers

Boolean

Multiple Assignment

No Value

Flow Control

If-Then-Else Condition Example

For-Loop Example

While-Loop Example

Data Structures

Tuple Example

List Example

Dictionary Example

Functions

NumPy Crash Course

Create Array

Access Data

Arithmetic

Matplotlib Crash Course

Line Plot

Scatter Plot

Pandas Crash Course

Series

DataFrame

Summary

Discover Fast Machine Learning in Python!

Develop Your Own Models in Minutes

Finally Bring Machine Learning To Your Own Projects

More On This Topic

24 Responses to Crash Course in Python for Machine Learning Developers

Leave a Reply Click here to cancel reply.

Finally Bring Machine Learning To
Your Own Projects